maad.util.plot_features

maad.util.plot_features(df, ax=None, norm=True, mode='24h', **kwargs)[source]

Plot the variation of features values (ie. indices) in the DataFrame obtained with maad.features.

Parameters:

dfPanda DataFrame

DataFrame with features (ie. indices).

normboolean, default is True

if True, the features are normalized by the max

modestring in {‘24h’}, default is ‘24h’

Select if the timeline of the phenology :: -‘24h’ : average of the results over a day - otherwise, the timeline is the timeline of the dataframe

**kwargs

figsizetuple of integers, optional, default: (4,10)
width, height in inches.
figtitlestring, optional, default‘’
title of the figure
xlabelstring, optional, default‘Time [s]’
label of the horizontal axis
ylabelstring, optional, default‘Amplitude [AU]’
label of the vertical axis
xtickstuple of ndarrays, optional, defaultnone
- ticks : array_like => A list of positions at which ticks should be placed. You can pass an empty list to disable yticks.
- labels : array_like, optional => A list of explicit labels to place at the given locs.
ytickstuple of ndarrays, optional, defaultnone
- ticks : array_like => A list of positions at which ticks should be placed. You can pass an empty list to disable yticks.
- labels : array_like, optional => A list of explicit labels to place at the given locs.
nowboolean, optional, defaultTrue
if True, display now. Cannot display multiple images. To display mutliple images, set now=False until the last call for the last image

… and more, see matplotlib

Returns:

figFigure: The Figure instance
axAxis: The Axis instance

Examples

see plot_extract_alpha_indices.py advanced example for a complete example

>>> import numpy as np
>>> import pandas as pd
>>> np.random.seed(2021)
>>> M = np.random.rand(24, 2)
>>> df = pd.DataFrame(M)
>>> indices = ['A','B']
>>> df.columns = indices
>>> df.index =pd.date_range(start=pd.Timestamp('00:00:00'), end=pd.Timestamp('23:00:00'), freq='1H')
>>> maad.util.plot_features(df) 